The Study Of Seasonal Variations Of Leaf Area Index In Semiarid Shrub Communities

Leaf area index (LAI) in two semiarid shrub communities dominated with Artemisia ordosica, Salix cheilophila, Hedysarum scoparium, Hedysarum fruticosum var. mongolicum, Agropyron cristatum at Yanchi, Ningxia was measured with both direct (allometric equations) and indirect (Li-2000canopy analyzer) method throughout the growing season of2012and2013. We also measured leaf characteristics of those shrub species throughout these periods of time. Our objectives were to compare direct and indirect measurements, to explore how actual leaf area index (LAIa) values from allometric equations can be used to calibrate effective leaf area index(LAIe) measured by Li-2000, and to model seasonal LAI variations using climatic variables.Results showed that bivariate functions of height and canopy area generally better predicted individual total leaf area than functions of canopy area alone for Artemisia ordosica. Trivariate functions of leaf branch longth, branch longth and basical diameter generally better predicted individual total leaf area than functions of leaf branch longth and basical diameter for Salix cheilophila and Hedysarum scoparium. On Community level, both LAIe and LAIa showed hump-shaped seasonal patterns. A mid-season stable period started from early July and ended in mid-September. LAIe was always larger than LAIa over the growing season. LAIe and LAIa showed a two-phase linear relationship over the season, separated by late August. LAIe and LAIa showed a weak relationship with rainfall or soil water content in a growing season. LAle and LAIa varied with accumulated temperature above5℃(ΣD), first increasing with ΣD, and then being stable during the mid-season. LAIe and LAIa showed a negative exponential relationship with ρp/ρs. Our results contribute to the understanding of LAI patterns in semiarid communities, could aid in the development of efficient and accurate methods for measuring LAI, and provide ground-based data for calibrating remotely-sensed information and for parameterizing process-based ecosystem carbon cycle models.